week 9 self similarity


Here is my comparison for track 1 till 7 and 10 with a structure analysis of the playlist in which only John Cage is credited on Spotify.

  1. Is the original, you can see that it exist out of 3 parts. Track 3 is the closest (if you follow the big lines) in form to track 1. The other tracks are just all over the place and some of them could be a total different piece.

Week 9 sceptrogram


You can see very clear in map1 that there is a magnitude of 0 around c01. c02 has the highest magnitude overall.

Map track 2, 6, 10 and 4 in order of most to least have a similar magnitude aroung c02. It is noticable tht there is a magnitude around c03 in track 3 and 7. Track 5 could again be another piece.

Introduction

Dear Reader,

For this project, I want to analyse different versions of the musical piece: 4’33’’ by John Cage. 4’33’’ is a piece made by John Cage. It is not my favourite song, but it questions perfectly what music is and what it can be. I am interested in which information I can get about the different versions, by analysing it with Rstudios. That is why I made 4 playlists with different versions of 4’33’‘. The first playlist contains all the 57 versions I could find on Spotify, some of them are just parts of the pieces. The 2nd playlist are all the version made and played/performed by John Cage. The 3rd playlist are the versions that do not give credits to John Cage, but has the same or a similar name. The 4th playlist consist of interpretation of 4’33’’.

The lists that I use are these list: Playlist 1: https://open.spotify.com/playlist/34a7macvtzIMePk2fRq4oC?si=78984ccde61a4f44 Playlist 2: https://open.spotify.com/playlist/5sWJTo3Zxy9oTf5CCqc624?si=2db9da0f122a49ae Playlist 3: https://open.spotify.com/playlist/7qwMKRYgTiRLOLszqDYyj9?si=76be1e131ac94e7d Playlist 4:https://open.spotify.com/playlist/383bBCxqKWd8tpvWrU9yYH?si=ae28a61a0874436b

These are the libraries that I use: library(tidyverse) library(spotifyr) library(compmus)

I hope that I have informed you well enough and enjoy reading my analysis of 4’33”’s.

Kind regards, Lucius Groot

week 8

Week 8 part2


I do not know what I can conclude from the map tracks; but apparently every 4’33’’ has its own key; while I cannot hear a key if I am listening.

week 7

In week 7, I experiment with different codes. You can see what my results are in the following tabs:

Summary of the lists

# A tibble: 1 × 12
  mean_speechiness mean_acousticness mean_liveness sd_speechiness
             <dbl>             <dbl>         <dbl>          <dbl>
1               NA                NA            NA             NA
# ℹ 8 more variables: sd_acousticness <dbl>, sd_liveness <dbl>,
#   median_speechiness <dbl>, median_acousticness <dbl>, median_liveness <dbl>,
#   mad_speechiness <dbl>, mad_acousticness <dbl>, mad_liveness <dbl>
# A tibble: 1 × 12
  mean_speechiness mean_acousticness mean_liveness sd_speechiness
             <dbl>             <dbl>         <dbl>          <dbl>
1            0.180             0.234         0.316          0.180
# ℹ 8 more variables: sd_acousticness <dbl>, sd_liveness <dbl>,
#   median_speechiness <dbl>, median_acousticness <dbl>, median_liveness <dbl>,
#   mad_speechiness <dbl>, mad_acousticness <dbl>, mad_liveness <dbl>
# A tibble: 1 × 12
  mean_speechiness mean_acousticness mean_liveness sd_speechiness
             <dbl>             <dbl>         <dbl>          <dbl>
1           0.0417             0.445         0.204         0.0279
# ℹ 8 more variables: sd_acousticness <dbl>, sd_liveness <dbl>,
#   median_speechiness <dbl>, median_acousticness <dbl>, median_liveness <dbl>,
#   mad_speechiness <dbl>, mad_acousticness <dbl>, mad_liveness <dbl>
# A tibble: 1 × 12
  mean_speechiness mean_acousticness mean_liveness sd_speechiness
             <dbl>             <dbl>         <dbl>          <dbl>
1               NA                NA            NA             NA
# ℹ 8 more variables: sd_acousticness <dbl>, sd_liveness <dbl>,
#   median_speechiness <dbl>, median_acousticness <dbl>, median_liveness <dbl>,
#   mad_speechiness <dbl>, mad_acousticness <dbl>, mad_liveness <dbl>
# A tibble: 1 × 12
  mean_speechiness mean_acousticness mean_liveness sd_speechiness
             <dbl>             <dbl>         <dbl>          <dbl>
1               NA                NA            NA             NA
# ℹ 8 more variables: sd_acousticness <dbl>, sd_liveness <dbl>,
#   median_speechiness <dbl>, median_acousticness <dbl>, median_liveness <dbl>,
#   mad_speechiness <dbl>, mad_acousticness <dbl>, mad_liveness <dbl>
# A tibble: 1 × 12
  mean_speechiness mean_acousticness mean_liveness sd_speechiness
             <dbl>             <dbl>         <dbl>          <dbl>
1               NA                NA            NA             NA
# ℹ 8 more variables: sd_acousticness <dbl>, sd_liveness <dbl>,
#   median_speechiness <dbl>, median_acousticness <dbl>, median_liveness <dbl>,
#   mad_speechiness <dbl>, mad_acousticness <dbl>, mad_liveness <dbl>

The summary I let Rstudio made of my lists: It is possible to make a summary of 4’33’’, but not if there are too many songs in one playlist.

Valence/Energy/Mode

I want to know of both the lists: Valence versus energy + the mode


I will have to reconsider the setting of the plots.

Valence/Energy/Mode in a smoothcurve


I want to know of both the lists: How can there be a key? What makes that there are different energies and how can some be negative?

The code of the homework week 7

***

I took some inspiration of the homework for my longest code ever

Histogram of the lists

The histogram I made about both the lists:


nothing special

conclusion